From 4a9138e6422e3e85c60da424884b2eb13c791979 Mon Sep 17 00:00:00 2001
From: awinml <97467100+awinml@users.noreply.github.com>
Date: Sat, 8 Oct 2022 12:56:05 +0530
Subject: [PATCH 1/3] Ensures that sklearn.utils.extmath.fast_logdet passes
 numpydoc validation

---
 sklearn/tests/test_docstrings.py |  1 -
 sklearn/utils/extmath.py         | 52 +++++++++++++++++++++++++++++---
 2 files changed, 47 insertions(+), 6 deletions(-)

diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
index 591107502c296..bf660de17c659 100644
--- a/sklearn/tests/test_docstrings.py
+++ b/sklearn/tests/test_docstrings.py
@@ -12,7 +12,6 @@
 numpydoc_validation = pytest.importorskip("numpydoc.validate")
 
 FUNCTION_DOCSTRING_IGNORE_LIST = [
-    "sklearn.utils.extmath.fast_logdet",
     "sklearn.utils.extmath.randomized_svd",
     "sklearn.utils.extmath.svd_flip",
     "sklearn.utils.gen_batches",
diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
index 4afa3a5540f98..223ea7ef3002e 100644
--- a/sklearn/utils/extmath.py
+++ b/sklearn/utils/extmath.py
@@ -82,15 +82,57 @@ def row_norms(X, squared=False):
 
 
 def fast_logdet(A):
-    """Compute log(det(A)) for A symmetric.
+    """Compute logarithm of determinant of a square matrix.
 
-    Equivalent to : np.log(nl.det(A)) but more robust.
-    It returns -Inf if det(A) is non positive or is not defined.
+    The (natural) logarithm of the determinant of a square matrix
+    is returned if det(A) is non-negative and well defined.
+    If the determinant is zero or negative returns -Inf.
+
+    Log Det = log(det(A)) for A, where A is square matrix.
+
+    Equivalent to : np.log(np.det(A)) but more robust.
 
     Parameters
     ----------
-    A : array-like
-        The matrix.
+    A : array_like of shape (n, n)
+        Input array, has to be a square 2-D array.
+        The square matrix whose (natural) logarithm of the determinant
+        needs to be found.
+
+    Returns
+    -------
+    logdet : float
+        When det(A) is non-negative (natural) logarithm of the determinant
+        is returned. When the determinant is non-positive or not defined, then -inf
+        is returned.
+
+    See Also
+    --------
+    numpy.linalg.slogdet : Compute the sign and (natural) logarithm of the determinant
+        of an array.
+
+    Examples
+    --------
+    There can be three cases:
+    Case-1: Matrix with a Positive determinant (Value of the logarithm of the
+    determinant is returned)
+
+    >>> from sklearn.utils.extmath import fast_logdet
+    >>> a = np.array([[5, 1], [2, 8]])
+    >>> fast_logdet(a)
+    3.6375861597263857
+
+    Case-2: Matrix with a determinant equal to zero (-inf is returned)
+
+    >>> b = np.array([[1, 2], [1, 2]])
+    >>> fast_logdet(b)
+    -inf
+
+    Case-3: Matrix with a Negative determinant (-inf is returned)
+
+    >>> c = np.array([[1, 2], [3, 4]])
+    >>> fast_logdet(c)
+    -inf
     """
     sign, ld = np.linalg.slogdet(A)
     if not sign > 0:

From 099a3f7e30ccc8e955f522f6b68962f0e35e02b5 Mon Sep 17 00:00:00 2001
From: Ashwin Mathur <97467100+awinml@users.noreply.github.com>
Date: Sat, 8 Oct 2022 20:33:12 +0530
Subject: [PATCH 2/3] Update extmath.py

---
 sklearn/utils/extmath.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
index 223ea7ef3002e..2d250e2d2a0af 100644
--- a/sklearn/utils/extmath.py
+++ b/sklearn/utils/extmath.py
@@ -117,6 +117,7 @@ def fast_logdet(A):
     Case-1: Matrix with a Positive determinant (Value of the logarithm of the
     determinant is returned)
 
+    >>> import numpy as np
     >>> from sklearn.utils.extmath import fast_logdet
     >>> a = np.array([[5, 1], [2, 8]])
     >>> fast_logdet(a)

From 2a113a7028520beba088c08dd3350f17da509323 Mon Sep 17 00:00:00 2001
From: jeremie du boisberranger <jeremiedbb@yahoo.fr>
Date: Fri, 14 Oct 2022 11:54:21 +0200
Subject: [PATCH 3/3] simplify some parts, example in particular

---
 sklearn/utils/extmath.py | 27 +++------------------------
 1 file changed, 3 insertions(+), 24 deletions(-)

diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
index 09913b96e4e11..675c51844e01f 100644
--- a/sklearn/utils/extmath.py
+++ b/sklearn/utils/extmath.py
@@ -88,23 +88,18 @@ def fast_logdet(A):
     is returned if det(A) is non-negative and well defined.
     If the determinant is zero or negative returns -Inf.
 
-    Log Det = log(det(A)) for A, where A is square matrix.
-
     Equivalent to : np.log(np.det(A)) but more robust.
 
     Parameters
     ----------
     A : array_like of shape (n, n)
-        Input array, has to be a square 2-D array.
-        The square matrix whose (natural) logarithm of the determinant
-        needs to be found.
+        The square matrix.
 
     Returns
     -------
     logdet : float
-        When det(A) is non-negative (natural) logarithm of the determinant
-        is returned. When the determinant is non-positive or not defined, then -inf
-        is returned.
+        When det(A) is strictly positive, log(det(A)) is returned.
+        When det(A) is non-positive or not defined, then -inf is returned.
 
     See Also
     --------
@@ -113,27 +108,11 @@ def fast_logdet(A):
 
     Examples
     --------
-    There can be three cases:
-    Case-1: Matrix with a Positive determinant (Value of the logarithm of the
-    determinant is returned)
-
     >>> import numpy as np
     >>> from sklearn.utils.extmath import fast_logdet
     >>> a = np.array([[5, 1], [2, 8]])
     >>> fast_logdet(a)
     3.6375861597263857
-
-    Case-2: Matrix with a determinant equal to zero (-inf is returned)
-
-    >>> b = np.array([[1, 2], [1, 2]])
-    >>> fast_logdet(b)
-    -inf
-
-    Case-3: Matrix with a Negative determinant (-inf is returned)
-
-    >>> c = np.array([[1, 2], [3, 4]])
-    >>> fast_logdet(c)
-    -inf
     """
     sign, ld = np.linalg.slogdet(A)
     if not sign > 0: